Faroja/Machine-Learning-Practice-5

Practice Machine Learning Model Complexity in Linear Model

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Somnibyte/MLKit

A simple machine learning framework written in Swift 🤖

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wyattowalsh/regularized-linear-regression-deep-dive

Explanations and Python implementations of Ordinary Least Squares regression, Ridge regression, Lasso regression (solved via Coordinate Descent), and Elastic Net regression (also solved via Coordinate Descent) applied to assess wine quality given numerous numerical features. Additional data analysis and visualization in Python is included.

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alaa-aleryani/Final_Project

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akash1188/Predictive-Analysis-ML

Explore ML mini-projects with Jupyter notebooks. Discover predictive analysis for commercial sales, leveraging regression models such as linear regression, decision trees, random forests, lasso, ridge, and extra-trees regressor.

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exmnx/STA-HW

Work from various classes

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SriramChowdaryMogalapu/surpriseHousing

Regression(Lasso and Ridge) model using regularization.

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vishal017/Tourist_Place_Analysis

India tourist place analysis using Linear, Lasso and Ridge regression

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SANJAIKUMAR-28/LossoAndRidge_Regression

Both Lasso and Ridge Regression are commonly used regularization techniques in linear regression that address the issue of overfitting

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prakHr/NeuralNetworksAndFuzzyLogic

[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic

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ASADAYUB1/NYC-Taxi-Fare-Prediction-with-Ridge-Regression-Random-Forests-and-Gradient-Boosting

In this project, I've trained a machine learning model to predict taxi fares for rides in New York City. The model takes into account details such as pickup date and time, pickup and drop-off locations, and the number of passengers.

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lexxai/goit_python_ds_hw_04

Модуль 4. Класифікація та оцінка роботи моделі. Лінійна регресія: перенавчання та регуляризація

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Tarek0Hisham/Diamond-Dataset

'Diamond ' is one of the best-known and most sought-after gemstones. They have been used as decorative items since ancient times. Today, we are gonna make a model to expect its price from the Diamond dataset.

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dorisyan1122/Crime-Predictive-Modeling

Final Group Project for Advanced Data Science for Public Policy @ McCourt

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alexvlis/movie-recommendation-system

Movie Recommendation System using the MovieLens dataset

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MuskanKhandelia/Toyota_Corolla_Price_Prediction

supervised-machine-learning-multiple-linear-regression-prediction

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LuHG18/Predict-WAR

Analyze player statistics to forecast their future impact on team success, utilizing ML in Python.

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cankobanz/understanding-supercapacitor-charge-discharge-rates-using-machine-learning

The project aims to design machine learning algorithm which is able to predict energy densities of supercapacitors by using input data that is enriched from the results came from image processing techniques.

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sai-manas/Diamond_Price_Prediction

Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app

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AmirInt/ridgie

As part of the UCSanDiego online course "Machine Learning Fundamentals"

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letdatado/Machine-Learning

This repository contains my work on Machine Learning with Python using scikit-learn library

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Shipra-09/ML-Project-Multiple-Linear-Regression

Exploring Insights/Inferences by performing EDA on the given project data (50_Startups and Toyota Corolla data) . Model fitting via linear regression by Importing sklearn package. Selecting the best fitted model via python programming.

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ShafakatArnob/BDRiceStudy-ProductionAnalysis-PricePrediction

A Comprehensive Study of Rice Production and Price in Bangladesh: Geospatial Visualization and Predictive Modeling Techniques.

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jman-9/linear-regression-practice

Practice of Linear Regression

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SrutiDutta/Insurance_Cost_Prediction

Predicting Insurance Cost

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gsasikiran/Comparative-Evaluation-of-Pretrained-Transfer-Learning-Models-on-ASAG

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edbonneville/validation-failed-EVT

Code and supplementary materials for the manuscript "Development and validation of a prediction model for failure of the transfemoral approach of endovascular treatment for large vessel occlusion acute ischemic stroke" (2023, Cerebrovascular Diseases))

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n8tlmps/credit-risk-assessment

evaluating credit default rate using statistical machine learning methods

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marcNY/LinearRegMLCourse

Language: Python - Size: 3.91 KB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 0 - Forks: 1

dhanvina/EquiSight

Unveiling the Art of Stock Market Prognostication through Regression Algorithms. Delve into our research exploring the power of machine learning in predicting market trends. Discover the secrets behind top regression models like Linear, Robust, Ridge, and Lasso Regression. Unravel the complexities of the market with data-driven precision.

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tristantcooper/MAST30034_Assignment1

Coding work for MAST30034 2021 Assignment 1.

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sachink1729/Regression-Algorithms

This is my college practice work, where i try to learn and cover all the regression algorithms (preferably in python)

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simran2097/ServiceNow-Stock-Prediction

Machine Learning & Python in Finance

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ahmad-sohrabi/regression_classification

It is a project with 2 parts. The First Part is a Regression & the second part is a classification problem. Datasets are obtained from Kaggle.

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Shivank1006/Regression_Analysis_on-Boston-House_Dataset

Data Science Assignment

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OmkarKadam2002/Regression-Attempt-

House Prices using 6 Regression models

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MohammedAhdal/Predicting-Taxi-out-Delay

Kaggle task on JFK dataset for supervised machine learning (Linear Regression)

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TaoSunVoyage/Higgs_ML

Higgs Boson Machine Learning Challenge - Numpy only solution

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SrutiDutta/Ridge_Regression--Delivery-Time-Analysis

Using Ridge Regression & Simple Linear Regression to predict Delivery time

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nekcht/ml-classic-scratch

Ordinary Least Squares, Ridge Regression, Expectation Maximization, Full Bayesian Inference, Bayes Classifiers, kNN, and MLP core algorithms from scratch. Some auxiliary functions are also used.

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Alexdruso/II2202-Research-Quantum-ML-Sanvito-Stuart

This repository contains the code for the experiments performed in the course II2202 Research Methodology and Scientific Writing at KTH. The experiments involve running quantum algorithms on DWave's quantum annealers.

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tboudart/Financial-Markets-Regression-Analysis

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.

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zabihin/Walmart_Sales

Block 3 - Walmart_Sales

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mukul-bhele/airqualityindex

Predicting Air Quality Index (AQI) using meteorological parameters and various machine learning models. Regression analysis with Linear Regression, Lasso, Ridge, Random Forest, XGBoost, and Artificial Neural Network (ANN).

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snigdhab7/RegressionAlchemy

Explore Linear Regression with Gradient Descent, Stochastic Gradient Descent, and Ridge Regression. Uncover algorithmic insights in data modeling. 📊🎶🚀

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Apaulgithub/Bike_Sharing_Demand_Prediction

This Machine Learning Project based on Rental Bike Sharing Demand Prediction required at each hour of the day so that stable supply of rental bikes can be made possible. This is done by applying various Regression Machine Learning Algorithms.

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SINGHxTUSHAR/Forest-Fire-Prediction-RidgeRegression

This model predicts the forest fire by using the Ridge-Regression algorithm.

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am-tropin/poland-apartment-prices

🇵🇱🏠 The project predicts an apartment price for Warsaw, Krakow and Poznan. Distributed apartments by districts using geopandas; built XGBoost model with MAPE = 9% (the best of others).

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fatih-ml/car-price-prediction-linear-regression-algorithms

Explore accurate car price prediction through advanced feature engineering and linear regression algorithms, unraveling insights from a 2019 dataset sourced from a prominent online car trading company.

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nikhilbordekar/Yes-Bank-s-Stock-Closing-Price-Prediction-by-Regression

This project focuses on forecasting the closing prices of Yes Bank's stock. Through data analysis and predictive modeling, this project provides valuable insights for investors and traders, aiding them in making informed decisions about their investments in Yes Bank's stock.

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NikolaAndro/Water_Quality_Prediction

Predicting the water quality index based on the parameters that are acquired from waters of India.

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romanwerpachowski/ML

ML++ and cppyml: efficient implementations of selected ML algorithms, with Python bindings.

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mzachow/wheat-yield-forecast-brazil

Ridge regression to forecast wheat yield variabilities for Brazil using observed and forecasted climate data.

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zhangchicheng/Machine-Learning-in-Numpy

Implement Basic Machine Learning Algorithms from Scratch

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Owinnie/MLearn

Machine Learning projects

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Bhevendra/ML-Regression

Regression on BOSTON dataset from sklearn

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unnatibshah/LASSO-and-Boosting-for-Regression

LASSO and Boosting for Regression on Communities and Crime data

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vivek2319/Advanced-Regression-Methods

This repository focuses on different linear regression methods which are uncommon.

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MBKraus/Predicting_real_estate_prices_using_scikit-learn

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

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0x4249/LMBGE

Julia code for running the numerical experiment in Subsection 4.6.2 of Brian Irwin's PhD thesis.

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ferdiUP/machine-learning

Classical ML algorithms implementation.

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pierogio/ML_regressions

Application of Machine Learning Regression to Housing Data.

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Vivikt-573/House_Price_Prediction

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theablemo/ML-assignments

This repository contains the practical assignments for the Machine Learning course taught by Dr.Mohsen Ansari in Spring semester of 2023 at Sharif University of Technology

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YiranJing/Coronavirus-Epidemic-COVID-19

👩🏻‍⚕️Covid-19 estimation and forecast using statistical model; 新型冠状病毒肺炎统计模型预测 (Jan 2020)

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sonwaneshivani/Fire-Weather-Index-Prediction

ML Regression application built using Flask

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Labo-Lacourse/Code_chap_23_logistic_regression_regularization

Algorithmes d’apprentissage et modèles statistiques: Un exemple de régression logistique régularisée et de validation croisée pour prédire le décrochage scolaire

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annieyan/Stochastic_dual_coordinate_ascent

classify mnist datasets using ridge regression, optimize the algorithem with SGD, stochastic dual coordinate ascent, and mini-batching

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Moozzaart23/py-Regressor

Implementation of Gradient-Descent algorithms in python from scratch

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ashishyadav24092000/LInear-Ridge-Lasso-Regression

Performing all the three regression i.e. Linear, Ridge, Lasso for a dataset.

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xadityax/ML-Models-from-scratch

Various machine learning models written from scratch.

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xadityax/MLE-and-Simple-Linear-Regression

Foundations of Data Science. Linear regression, Logistic Regression implemented from scratch. Lasso and Ridge regression.

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marcotav/machine-learning-regression-models

This repository contains only projects using regression analysis techniques. Examples include a comprehensive analysis of retail store expansion strategies using Lasso and Ridge regressions.

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gosuddin/machine-learning-for-finance

ML Coursework focused on solving Computational Finance and Risk Assessment models

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vaitybharati/Ridge_Lasso_ElasticNet

Model Building and Testing using Ridge, Lasso and ElasticNet Methods

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NikhilaThota/CapstoneProject_House_Prices_Prediction

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

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sudeshnapal12/Machine-Learning-algorithms-Matlab

Contains ML Algorithms implemented as part of CSE 512 - Machine Learning class taken by Fransico Orabona. Implemented Linear Regression using polynomial basis functions, Perceptron, Ridge Regression, SVM Primal, Kernel Ridge Regression, Kernel SVM, Kmeans.

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2015xli/linear-regression

A simple tutorial on linear regression in python

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Nemshan/predicting-Paid-amount-for-Claims-Data

Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.

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SiluPanda/ridge-and-lasso-regression

implementation of ridge and lasso regression from scratch

Language: Python - Size: 133 KB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 0 - Forks: 1

Apaulgithub/CodeClauseInternship_Price_Recommendation

Code Clause Internship Project: Optimize your online selling strategy with the machine learning-based price recommendation tool, maximizing competitiveness and revenue.

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fbalensiefer/Machine_Learning_R

Statistical Learning in R

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mayankparihar98/Car_Price_Prediction

A simple Streamlit WebApp, which can predict Car Price with Machine Learning Models such as Linear Regression, Ridge Regression, and Lasso Regression.

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giuseppe-coco/ValorediMercato

Regressor predicting the market value of footballers

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lukebella/SpotifyRegression

Implementation of (Kernel) Ridge Regression predictors from scratch on Kaggle's Spotify Tracks Dataset.

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aakashsyadav1999/online-retail-dataset

This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

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IssamLL/Wining-Solution-ENSIAS-AI-ML-Competition

This repository contains the winning solution for the Energy Consumption Prediction Kaggle competition, securing second place.

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TheGreatRico/time-series-prediction

Predicting time series using various models.

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rj425/House-Prices-Prediction

This repository contains the code for house prices prediction competition hosted on Kaggle.

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nemolino/TrackPopularityPredictor

Statistical Methods for Machine Learning project

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sfansaria/Machine-Learning

ols_regression, Simple_Linear_Regression,univariate_Polynomial_Regression,Bayesian_Regression

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DSkapinakis/supplier-selection-ml-models

Development of a predictive model that selects the most cost efficient supplier for a given task

Language: Python - Size: 228 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

yasarigno/Predictions_on_Energy_Consumption_in_Seattle

Prediction on energy consumptions of the city of Seattle in order to reach its goal of being a carbon neutral city in 2050.

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Abir0810/firm_business_prediction_model

Language: Python - Size: 130 KB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

Abir0810/bloom-model-using-regression-analysis

Predict whether it will bloom or not , it will be predicted by regression analysis.

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faizanxmulla/machine-learning-techniques

Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.

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natmurad/regularization

Regularization methods on R

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ShreyaPatil1199/Life_Expectancy

The overall objective of this project is to critically analyze and develop the relationships of quantitative factors affecting life expectancy in 193 countries between 2000 and 2015 that underlie changes in life expectancy. The importance of predicting life expectancy arises because of its important role as an indicator of the overall health.

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bhushan23/ADMM

Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression

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MathCortes/Projeto9-BostonHousing-ML_Regression

Nesse trabalho vou explorar uma conhecida base, boston dataset. Nela encontramos informações sobre algumas características de casas. Queremos estudar o comportamento dos preços desses imóveis para futuramente conseguirmos prever seus preços

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